The effects of data balancing approaches: A case study

[1]  R. Augusti,et al.  A novel strategy for the detection of boldenone undecylenate misuse in cattle using ultra-high performance liquid chromatography coupled to high resolution orbitrap mass spectrometry: from non-targeted to targeted. , 2021, Drug testing and analysis.

[2]  M. Pezzolato,et al.  Anabolic treatments in bovines: quantification of plasma protein markers of dexamethasone administration , 2021, Proteomics.

[3]  E. Marengo,et al.  Profiling of transcriptional biomarkers in FFPE liver samples: PLS-DA applications for detection of illicit administration of sex steroids and clenbuterol in veal calves , 2021 .

[4]  Ranjeet Kumar Ranjan,et al.  Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms , 2021, J. Exp. Theor. Artif. Intell..

[5]  A. Benedetto,et al.  Omics applications in the fight against abuse of anabolic substances in cattle: challenges, perspectives and opportunities , 2021 .

[6]  Ning Li,et al.  A wind turbine frequent principal fault detection and localization approach with imbalanced data using an improved synthetic oversampling technique , 2021, International Journal of Electrical Power & Energy Systems.

[7]  Linlin You,et al.  Commercial Vehicle Activity Prediction With Imbalanced Class Distribution Using a Hybrid Sampling and Gradient Boosting Approach , 2021, IEEE Transactions on Intelligent Transportation Systems.

[8]  S. Stürzenbaum,et al.  Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. , 2021, Analytical methods : advancing methods and applications.

[9]  Lars M Blank,et al.  Machine Learning Applications for Mass Spectrometry-Based Metabolomics , 2020, Metabolites.

[10]  Lennart Martens,et al.  The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows , 2020, Proteomics.

[11]  C. L. Drum,et al.  Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring , 2020, Advanced Therapeutics.

[12]  Stephen Gorard,et al.  Handling missing data in numeric analyses , 2020, International Journal of Social Research Methodology.

[13]  Aydin Kaya,et al.  The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models , 2019, J. Softw. Evol. Process..

[14]  Taghi M. Khoshgoftaar,et al.  Survey on deep learning with class imbalance , 2019, J. Big Data.

[15]  Brett A. Lidbury,et al.  Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines , 2017, BMC Medical Informatics and Decision Making.

[16]  Tian C. Zhang,et al.  Effect of growth promotants on the occurrence of endogenous and synthetic steroid hormones on feedlot soils and in runoff from beef cattle feeding operations. , 2012, Environmental science & technology.

[17]  Oral Alan,et al.  Thresholds based outlier detection approach for mining class outliers: An empirical case study on software measurement datasets , 2011, Expert Syst. Appl..

[18]  Colin R. Janssen,et al.  Characterisation of steroids in wooden crates of veal calves by accelerated solvent extraction (ASE®) and ultra-high performance liquid chromatography coupled to triple quadrupole mass spectrometry (U-HPLC-QqQ-MS-MS) , 2010, Analytical and bioanalytical chemistry.

[19]  M. Nielen,et al.  Metabolomics approach to anabolic steroid urine profiling of bovines treated with prohormones. , 2009, Analytical chemistry.

[20]  R. Angeletti,et al.  The Urinary Ratio of Testosterone to Epitetosterone: A Good Marker of Illegal Treatment also in Cattle? , 2006, Veterinary Research Communications.

[21]  P. Patrician Multiple imputation for missing data. , 2002, Research in nursing & health.

[22]  L. Lucentini,et al.  Quantitation of anabolic hormones and their metabolites in bovine serum and urine by liquid chromatography-tandem mass spectrometry. , 2000, Journal of chromatography. A.

[23]  Xiaomei Li,et al.  A Novel Ensemble Learning Paradigm for Medical Diagnosis With Imbalanced Data , 2020, IEEE Access.

[24]  Ozgur Koray Sahingoz,et al.  Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset , 2020, IEEE Access.

[25]  James J. Chen,et al.  Class-imbalanced classifiers for high-dimensional data , 2013, Briefings Bioinform..

[26]  Banu Diri,et al.  Metrics-Driven Software Quality Prediction Without Prior Fault Data , 2010 .